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Re outliers
This scenario probably means that they are not ‘outliers’ bt an intrinsic part of a non-normal distribtion
Try transformations e.g. Log, or box and cox
But as bruce says, tehn look for multivariat oltliers
Re: Detecting outliers; when to stop? This scenario probably means that they are not ‘outliers’ bt an intrinsic part of a non-normal distribtion
Try transformations e.g. Log, or box and cox
But as bruce says, tehn look for multivariat oltliers
Best
diana
On 16/04/2012 15:34, "Bruce Weaver" <MailScanner has detected a possible fraud attempt from "bruce.weaver@..." claiming to be bruce.weaver@... <http://bruce.weaver%40hotmail.com> > wrote:
noxeon wrote
>
> So I have detected some outliers in my data and removed them, however when
> I removed them all, new ones appeared in bloxplot, should I keep removing
> until there is none or just once is enough?
>
What's the variable (or variables)? Have you ruled out data entry errors?
What kind of analysis are you doing, some kind of regression model? If so,
I'd be more concerned about multivariate outliers and influential points
than univariate outliers. Cook's distance is one well-known measure of
influence you could look at.
HTH.
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Best
diana
Emeritus Professor Diana Kornbrot
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